Abstract
Biomedical electronic signals play an important role in clinical diagnosis. EEG as one kind of biomedical electronic signals has been widely used for an epilepsy diagnosis. EEG data are strongly characterized by the inner cluster style. The classic clustering-based detection techniques such as FCM, K-means, and AP cannot effectively partition the manifold data without considering the inner cluster style. Therefore, in this paper, we propose a novel stylistic data-driven possibilistic fuzzy clustering technique (SD-PFC). SD-PFC has its merits in two aspects: (1) a stylistic standardization matrix is used to represent the stylistic information of samples contained in the inner clusters. (2) The distance matrix is re-constructed by samples which are transformed by style normalization matrices. Extensive experiments on artificial datasets and real-life datasets show the effectiveness of the novel clustering technique.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
